6.S963 Final Paper 7/1/2024

Katie Chen, Teodor Nicola Antoniu
Massachusetts Institute of Technology

Abstract

Brain-Computer Interface (BCI) technologies represent a frontier in enhancing cognitive and physical capabilities, with potential applications ranging from medical treatments to productivity enhancement. This paper provides an overview of the BCI industry, including a survey of key players pushing
the boundaries of BCI applications. Subsequently, we focus on a detailed case study of Pison, a Boston-based startup, which aims to revolutionize cognitive performance through AI-fused wearable devices. Pison’s technology, which integrates electroneurography sensors with software analytics, is designed to inform users about their cognitive performance cycles by providing real-time feedback on their physiological signals. The startup aims to help customers optimize their schedules and lifestyles, including sleep patterns, to enhance their cognitive performance and align key activities with their peak cognitive performance periods. We critically examine challenges related to data analytics, data quality,
user engagement, and privacy, alongside the implications of data homogeneity and the potential biases it introduces. The ethical, legal, and social implications of BCIs are thoroughly discussed, emphasizing the importance of maintaining user privacy, ensuring data security, and managing bias to foster equitable technology access. In conclusion, the success of BCIs hinges on a balanced approach that values innovation alongside ethical responsibility and societal acceptability. By integrating rigorous testing, user-centered design, and adherence to ethical standards, BCIs can be responsibly incorporated into society to provide medical treatment and to enhance human capabilities while respecting individual autonomy and promoting equity.

This short paper explores the impact of open-source LLMs on the AI landscape, examining their development, the challenges they address, and the opportunities they present for the future of AI research and application. .

Full Paper

Disclaimer

This paper was written for Alfred Spector’s MIT Spring 2024 course 6.S963 Beyond Models – Applying Data Science/AI Effectively. It has not been peer-reviewed, and it may contain errors.